Hi, I'm
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Software Engineer building distributed systems and AI-powered tooling — from high-scale payments to LLM-driven automation. Focused on reliability, cost, and developer productivity.
How I work & what I bring
A quick snapshot for hiring managers — details in experience and skills.
I'm a Software Engineer with 3+ years of experience building distributed systems and AI-powered developer tooling—growing from backend engineering to AI-driven platforms.
I build LLM-powered applications, working across product, infrastructure, and security to deliver reliable and scalable systems—from payment pipelines to autonomous code review agents.
My core strengths are microservices, event-driven systems, and automation, with hands-on experience in Java, Python, Node.js, Kafka, and modern AI stacks like LangChain and RAG.
I focus on measurable impact—improving system performance, reducing costs, and enabling teams to ship faster with confidence.
Software Engineer
Illinois Institute of Technology
Education
M.S. in Computer Science, Illinois Institute of Technology (Aug 2023 – May 2025)
Experience
3+ years building distributed systems and AI-powered developer tooling
Expertise
Microservices, event-driven systems, Java, Python, Node.js, Kafka, LangChain, and RAG
Focus
Measurable impact—performance, cost, and helping teams ship faster with confidence
Professional Journey
Full-time roles only — each card starts with impact metrics recruiters can skim in under a minute.
AI Software Engineer
PR turnaround
18h → 2.5h
Engineers on reviews
200
Review cost / PR
$50 → $0.40
Pipeline reliability
99.8%
Highlights & scope
- >Lead development of an autonomous code review agent using LangChain Agents and OpenAI GPT-4o on FastAPI, orchestrating parallel security and performance scans that reduce PR turnaround from 18 h to 2.5 h.
- >Engineer event-driven webhook pipeline in Node.js and Express within a microservices architecture, processing GitHub PR events and dispatching payloads to the Python agent service via REST APIs, enabling real-time reviews for 200 engineers.
- >Implement RAG-based codebase search using a vector database and LangChain retrieval chains integrated with the GitHub REST API, providing contextual diff analysis that identifies 34 critical security vulnerabilities missed during manual review cycles.
- >Develop confidence-scored suggestion engine with automated test generation using LangChain structured outputs, mentoring 3 juniors on evaluation patterns while persisting audit trails in PostgreSQL with SQL analytics achieving 67% acceptance rate.
- >Build admin analytics dashboard in React and Next.js with product and engineering leadership to define acceptance-rate, false-positive, and cost-per-PR metrics, reducing review cost from $50 to $0.40 per PR.
- >Containerize microservices with Docker and deploy to AWS ECS, Lambda, and Kubernetes (EKS), integrating Slack REST API notifications with risk scoring that improves developer satisfaction from 3.2 to 4.6 out of 5.0.
- >Orchestrate end-to-end CI/CD pipelines using GitHub Actions with automated integration tests and agent quality gates, deploying across AWS infrastructure in Agile sprints with 99.8% pipeline reliability processing 600+ monthly PRs.
Software Engineer
Daily transactions
500K+
Payment failures
12% → 4%
Annual recovery
$2.1M
Reconciliation
48h → 2h
Highlights & scope
- >Led a 4-engineer squad to architect payment processing core in Java, Spring Boot, and Hibernate, implementing intelligent retry and multi-gateway routing across UPI rails reducing failure rates from 12% to 4%, recovering $2.1M annually.
- >Drove system design of event-driven microservices using Apache Kafka, streaming payment events, settlement triggers, and refund workflows across 6 consumer services processing 500K+ daily transactions with guaranteed ordering.
- >Built settlement reconciliation engine in Python and FastAPI, consuming Kafka events and automating bank file matching against PostgreSQL and MySQL ledgers with optimized indexing, reducing reconciliation turnaround from 48 h to 2 h.
- >Developed merchant transaction dashboard in React and TypeScript with real-time payment tracking, settlement summaries, and GMV analytics via REST APIs, collaborating with product and design to serve 25K+ merchants.
- >Engineered refund orchestration and webhook services in Node.js and Express, managing state machines with idempotency keys and Redis-backed rate limiting via REST APIs, reducing refund processing from 7 days to under 24 h.
- >Established comprehensive JUnit test suites achieving 90%+ coverage across Spring Boot payment-critical services, containerized with Docker, and deployed to AWS ECS via Jenkins and GitHub Actions CI/CD pipelines in Agile sprints.
- >Streamed transaction lifecycle events from Kafka into DynamoDB audit trails with TTL retention, enforced Git review gates with mandatory approvals for payment-critical paths, enabling PCI-DSS compliance across 500K+ daily transactions.
Technical Expertise
Keyword-aligned for ATS and recruiter search — maps directly to the stacks in my experience.
Programming Languages
Backend & API Development
Frontend Development
AI/ML & LLM Engineering
Databases & Event-Driven Systems
Cloud & DevOps
Testing, Quality & Observability
Tools & Collaboration
Featured Projects
Production-style work sample at the top; open-source and academic projects below — each lists stack so screeners can match reqs fast.
Autonomous Code Review & Engineering Intelligence
Production platform for AI-assisted code review: LangChain agents and GPT-4o on FastAPI, Node/Express webhook pipelines for GitHub PRs, RAG over repositories for contextual diffs, PostgreSQL-backed audit and analytics, and React/Next.js dashboards—paired with Docker, AWS (ECS, Lambda, EKS), Slack integrations, and GitHub Actions CI/CD with quality gates.
Technologies Used
Call Center Analytics Dashboard
Full-stack AI dashboard that analyzes 451 call center interactions using TypeScript, Express, React, and Gemini 2.5. Surfaces LLM insights, funnels, and revenue modeling so teams can spot patterns and coaching opportunities quickly. Built end-to-end with Cursor to iterate fast with AI-assisted development.
Technologies Used
RAG-Based Chatbot with AgentCore
Custom RAG chatbot on AWS Bedrock and AgentCore for domain-specific Q&A over PDFs and text files. Built embedding pipelines and retrieval logic to keep answers grounded in the knowledge base. Exposed a Python interface for real-time agent invocation and scalable reasoning workflows.
Technologies Used
AI Guest Concierge Agent
AI concierge agent that answers guest questions, retrieves event/vendor info, and automates end-to-end workflows. Uses a RAG pipeline on Pinecone + Supabase to keep responses relevant and reduce manual support load. Connects frontend UI to backend APIs for smooth, real-time interaction.
Technologies Used
Real-Time Hand Sign Detection System
Real-time gesture recognition system that detects 36 hand signs with 90%+ accuracy. Combines MediaPipe hand landmarks with a TensorFlow classifier for fast inference. Designed as a modular pipeline so models can be retrained, tuned, and deployed with minimal friction.
Technologies Used
Traffic Management System (CLI Analytics Tool)
Python + SQL CLI analytics tool for traffic incident, vehicle, road, and signal datasets. Uses normalized schemas and query-driven workflows to analyze violation density, coverage gaps, and route throughput. Gives operators a simple way to surface trends and make data-driven decisions.
Technologies Used
Career Progression
From scalable backend systems to AI-powered developer tooling.
Built and scaled backend systems for payments handling 500K+ daily transactions using microservices and event-driven architecture.
Improved reliability and enabled real-time workflows across payments, refunds, and reconciliation systems.
Strengthened expertise in distributed systems, machine learning, and scalable engineering.
Transitioned focus from backend development to AI-driven product engineering.
Building LLM-powered agents and RAG systems to automate code reviews and enhance developer productivity.
Designing scalable systems with modern cloud and microservices architecture, driving efficiency and quality at scale.
Academic Background
Master's in Computer Science (Aug 2023 – May 2025) — complements full-time experience in production systems and AI platforms.
Master of Science in Computer Science
Master'sKey Coursework
Graduate studies in computer science with emphasis on artificial intelligence, machine learning, and building reliable software systems.
Academic Highlights
Master of Science in Computer Science
Graduate program at Illinois Institute of Technology, Chicago, IL
Technical depth
Coursework spanning ML, systems, and software engineering practice
Strong CS foundation
Built on algorithms, AI/ML, and rigorous engineering fundamentals
Let's talk
Contact Information
Availability
Monday - Friday
9:00 AM - 6:00 PM CST
Weekend
Available for urgent matters
Usually responds within 24hrs
Send me a message
Have a project in mind? Let's discuss how we can work together.